Abstract
Next-generation sequencing (NGS) technologies have led to a huge amount of genomic data that need to be analyzed and interpreted. This fact has a huge impact on the DNA sequence alignment process, which nowadays requires the mapping of billions of small DNA sequences onto a reference genome. In this way, sequence alignment remains the most time-consuming stage in the sequence analysis workflow. To deal with this issue, state of the art aligners take advantage of parallelization strategies. However, the existent solutions show limited scalability and have a complex implementation. In this work we introduce SparkBWA, a new tool that exploits the capabilities of a big data technology as Spark to boost the performance of one of the most widely adopted aligner, the Burrows-Wheeler Aligner (BWA). The design of SparkBWA uses two independent software layers in such a way that no modifications to the original BWA source code are required, which assures its compatibility with any BWA version (future or legacy). SparkBWA is evaluated in different scenarios showing noticeable results in terms of performance and scalability. A comparison to other parallel BWA-based aligners validates the benefits of our approach. Finally, an intuitive and flexible API is provided to NGS professionals in order to facilitate the acceptance and adoption of the new tool. The source code of the software described in this paper is publicly available at https://github.com/citiususc/SparkBWA, with a GPL3 license.
Highlights
The history of modern DNA sequencing starts more than thirty-five years ago
SparkBWA is evaluated in terms of performance, scalability, and memory consumption
SparkBWA is on average 1.9× and 1.4× faster than SEAL and pBWA respectively
Summary
The history of modern DNA sequencing starts more than thirty-five years ago. These years have seen amazing growth in DNA sequencing capacity and speed, especially after the appearance of next-generation sequencing (NGS) and massive parallel sequencing in general. NGS platforms are evolving very quickly, pushing the sequencing capacity to unprecedented levels To address this challenge we propose to take advantage of parallel architectures using big data technologies in order to boost performance and improve scalability of the sequence aligners. NGS professionals demand solutions to perform sequence alignments efficiently in such a way that the implementation details are completely hidden to them For this reason SparkBWA provides a simple and flexible API to handle all the aspects related to the alignment process. SparkBWA has been evaluated both in terms of performance and memory consumption, and a thorough comparison between SparkBWA and several state-of-art BWA-based aligners is provided Those tools take advantage of different parallel approaches as Pthreads, MPI, and Hadoop to improve the performance of BWA.
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